Automated Torn Image Reassembly Using Shape Masks and Content Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for reassembling and repairing torn printed images are manual, time-consuming, costly, and non-repeatable, often requiring skilled professionals and lacking in accuracy, especially when novice users attempt to restore images without adequate backup copies or additional reference materials.
Innovation Solution
An automated system that scans torn image sections, generates shape masks to match image pieces based on contours, resolves ambiguities using content features, and fills gaps with material from user libraries or external images, enabling automatic reassembly and repair without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to reassemble and repair torn image pieces, then the quality of repair can be high when performed by trained professionals, but the process requires significant time and effort and is non-repeatable
Solution Approach 1:
The system enables automatic self-service image repair by using algorithms to autonomously identify torn image pieces, match them based on shape and content features, and reconstruct the original image without requiring manual intervention from trained professionals, thereby eliminating the trade-off between quality and time
Solution Approach 2:
The patent replaces the manual mechanical process of image repair with an automated computational system that uses shape analysis, feature matching, and content-based algorithms to perform reassembly and repair tasks that were previously done manually by professionals
2Manufacturing precision
If trained professionals are hired to reassemble and repair torn image pieces, then acceptable results can be achieved, but the cost becomes prohibitively high
Solution Approach 1:
The system creates a digital copy of the torn image pieces through scanning, then performs automated analysis and reconstruction on the digital copies using computational algorithms, eliminating the need for expensive manual professional services while maintaining repair quality
3Adaptability or versatility
If manual repair methods are used, then flexibility in handling complex repair scenarios is possible, but the process is non-repeatable and results vary based on professional skill and mood
Solution Approach 1:
The automated system incorporates feedback mechanisms where the algorithm continuously evaluates the matching quality of image pieces, adjusts its matching strategy based on shape and content feature analysis, and iteratively improves the reconstruction until optimal results are achieved, ensuring repeatable and consistent outcomes
Solution Approach 2:
The system changes multiple parameters including shape descriptors, feature extraction parameters, and matching thresholds to adapt to different repair scenarios, allowing the automated system to handle various complexities while maintaining consistent and repeatable results through algorithmic control
Data Source
AI summary
A digital medium environment includes at least one computing device. Systems and techniques are described herein for reassembling and repairing image sections (e.g., torn pieces of an image) by generating masks for the image sections that do not include content of the image sections, and matching image sections along contours (e.g., edges or sides of the image sections) based on shapes of the masks, features of content extracted from the image sections, or combinations thereof, depending on whether an ambiguity is determined. An ambiguity is determined when not all image sections included in the scans are matched by shape, or are redundantly matched. A composite image is reassembled from the image sections based on matching image sections. Furthermore, a composite image is repaired by adding material to the composite image from an additional image (e.g., an image other than the composite image and the image sections).